This agent-based model explores the dynamics between human behavior and vaccination strategies during COVID-19 pandemics. It examines how individual risk perceptions influence behaviors and subsequently affect epidemic outcomes in a simulated metropolitan area resembling New York City from December 2020 to May 2021.

Agents modify their daily activities—deciding whether to travel to densely populated urban centers or stay in less crowded neighborhoods—based on their risk perception. This perception is influenced by factors such as risk perception threshold, risk tolerance personality, mortality rate, disease prevalence, and the average number of contacts per agent in crowded settings. Agent characteristics are carefully calibrated to reflect New York City demographics, including age distribution and variations in infection probability and mortality rates across these groups. The agents can experience six distinct health statuses: susceptible, exposed, infectious, recovered from infection, dead, and vaccinated (SEIRDV). The simulation focuses on the Iota and Alpha variants, the dominant strains in New York City during the period.

We simulate six scenarios divided into three main categories:
1. A baseline model without vaccinations where agents exhibit no risk perception and are indifferent to virus transmission and disease prevalence.
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This model is an extended version of the original MERCURY model (https://www.comses.net/codebases/4347/releases/1.1.0/ ) . It allows for experiments to be performed in which empirically informed population sizes of sites are included, that allow for the scaling of the number of tableware traders with the population of settlements, and for hypothesised production centres of four tablewares to be used in experiments.

Experiments performed with this population extension and substantive interpretations derived from them are published in:

Hanson, J.W. & T. Brughmans. In press. Settlement scale and economic networks in the Roman Empire, in T. Brughmans & A.I. Wilson (ed.) Simulating Roman Economies. Theories, Methods and Computational Models. Oxford: Oxford University Press.

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This NetLogo model simulates the spread of climate change beliefs within a population of individuals. Each believer has an initial belief level, which changes over time due to interactions with other individuals and exposure to media. The aim of the model is to identify possible methods for reducing climate change denial.

A formalized implementation of Halstead and O’Shea’s Bad Year Economics. The agent population uses one of four resilience strategies in an attempt to cope with a dynamic environment of stresses and shocks.

3.8 with Unis

（An empty output folder named “NETLOGOexperiment” in the same location with the LAKEOBS_MIX.nlogo file is required before the model can be run properly）
The model is motivated by regime shifts (i.e. abrupt and persistent transition) revealed in the previous paleoecological study of Taibai Lake. The aim of this model is to improve a general understanding of the mechanism of emergent nonlinear shifts in complex systems. Prelimnary calibration and validation is done against survey data in MLYB lakes. Dynamic population changes of function groups can be simulated and observed on the Netlogo interface.
Main functional groups in lake ecosystems were modelled as super-individuals in a space where they interact with each other. They are phytoplankton, zooplankton, submerged macrophyte, planktivorous fish, herbivorous fish and piscivorous fish. The relationships between these functional groups include predation (e.g. zooplankton-phytoplankton), competition (phytoplankton-macrophyte) and protection (macrophyte-zooplankton). Each individual has properties in size, mass, energy, and age as physiological variables and reproduce or die according to predefined criteria. A system dynamic model was integrated to simulate external drivers.
Set biological and environmental parameters using the green sliders first. If the data of simulation are to be logged, set “Logdata” as true and input the name of the file you want the spreadsheet(.csv) to be called. You will need create an empty folder called “NETLOGOexperiment” in the same level and location with the LAKEOBS_MIX.nlogo file. Press “setup” to initialise the system and “go” to start life cycles.

Schelling famously proposed an extremely simple but highly illustrative social mechanism to understand how strong ethnic segregation could arise in a world where individuals do not necessarily want it. Schelling’s simple computational model is the starting point for our extensions in which we build upon Wilensky’s original NetLogo implementation of this model. Our two NetLogo models can be best studied while reading our chapter “Agent-based Computational Models” (Flache and de Matos Fernandes, 2021). In the chapter, we propose 10 best practices to elucidate how agent-based models are a unique method for providing and analyzing formally precise, and empirically plausible mechanistic explanations of puzzling social phenomena, such as segregation, in the social world. Our chapter addresses in particular analytical sociologists who are new to ABMs.

In the first model (SegregationExtended), we build on Wilensky’s implementation of Schelling’s model which is available in NetLogo library (Wilensky, 1997). We considerably extend this model, allowing in particular to include larger neighborhoods and a population with four groups roughly resembling the ethnic composition of a contemporary large U.S. city. Further features added concern the possibility to include random noise, and the addition of a number of new outcome measures tuned to highlight macro-level implications of the segregation dynamics for different groups in the agent society.

In SegregationDiscreteChoice, we further modify the model incorporating in particular three new features: 1) heterogeneous preferences roughly based on empirical research categorizing agents into low, medium, and highly tolerant within each of the ethnic subgroups of the population, 2) we drop global thresholds (%-similar-wanted) and introduce instead a continuous individual-level single-peaked preference function for agents’ ideal neighborhood composition, and 3) we use a discrete choice model according to which agents probabilistically decide whether to move to a vacant spot or stay in the current spot by comparing the attractiveness of both locations based on the individual preference functions.

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The model reproduces the spread of environmental awareness among agents and the impact of awareness level of the agents on the consumption of a resource, like energy. An agent is a household with a set of available advanced smart metering functions.

An agent-based model for the diffusion of innovations with multiple characteristics and price-premiums

This model demonstrates the spread of collapse through a network. The model is abstract but has many applications in various fields.